code: def construct_model_from_args(args: Dict) -> Embeddings:
      """
      Deserializes the model from the model storage
      """
      target = args.pop("target", None)
      class_name = args.pop(
          "class", LangchainEmbeddingHandler.DEFAULT_EMBEDDING_CLASS
      )
      if class_name in EMBEDDING_MODELS:
          logger.info(
              f"Mapping the user friendly name {class_name} to the class name: {EMBEDDING_MODELS[class_name]}"
          )
          class_name = EMBEDDING_MODELS[class_name]
      MODEL_CLASS = get_langchain_class(class_name)
      serialized_dict = copy.deepcopy(args)
      serialized_dict.pop("input_columns", None)
      model = MODEL_CLASS(**serialized_dict)
      if target is not None:
          args["target"] = target
      args["class"] = class_name
      return model
  
file_path: mindsdb/mindsdb/integrations/handlers/langchain_embedding_handler/langchain_embedding_handler.py
imports: import copy
  import importlib
  from typing import Dict, Union
  import pandas
  from langchain.embeddings.base import Embeddings
  from pandas import DataFrame
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
label_desc: Use image node_id 2 for calling a global function with example usage: construct_model_from_args(args) and returns: model